10 results about "Restrict boltzmann machine" patented technology

The invention provides a flower quality grading quality inspection method and system based on AI and image processing, and belongs to the technical field of computer vision and pattern recognizing.The method comprises the steps that an original image of the surface of a flower is collected through high-resolution imaging equipment, and high-frequency sub-band data representing tiny physical characteristics are extracted through multistage discrete wavelet transform; meanwhile, a sliding window is adopted to traverse the image, the color information entropy of a local area is calculated, and a color entropy graph is constructed. And after vectoring and splicing the two types of features, inputting the two types of features into a deep belief network model based on a multilayer restricted Boltzmann machine, and extracting deep abnormal feature codes. And finally, performing linear discriminant analysis on the code by utilizing a classification projection vector based on inter-class and intra-class distance optimization to generate an insect attack infection index, and realizing automatic grading quality inspection of the flower quality according to the insect attack infection index. According to the method, precise recognition and quantitative grading of tiny insect pests and recessive lesions on the surfaces of the fresh flowers are realized.

Embodiments of the present disclosure relate to a question and answer method, an electronic device and a computer program product. The method comprises: determining an answer base associated with a question; determining a restricted Boltzmann machine associated with the answer base, the restricted Boltzmann machine being used to determine a question set that the answer base can answer and a relationship between a question in the question set and the answer base; and determining, using the restricted Boltzmann machine, description information associated with the question for the answer base. Using the technical solution of the present disclosure, the description information for the answer base can be determined at the same time as the answer base associated with the question is determined, and the customer service personnel can be made to have a more thorough understanding of the determined answer base and obtain targeted recommendation information by providing the determined description information to the customer service personnel, thereby improving the user experience of the customer service personnel using the question and answer system.

A first computing node of a system can receive sensor data about a physical environment. The first computing node can analyze the sensor data with a restricted Boltzmann machine (RBM) neural network to determine whether there is a fault condition in the physical environment, an identification of the fault condition being omitted from data used to train the RBM neural network. The first computing node can update the RBM neural network based on the sensor data to produce a first updated RBM neural network. The first computing node can send a first patch indicative of the first updated RBM neural network to a central server. The first computing node can receive, from the central server, information indicative of a second updated RBM neural network, the second updated RBM neural network being based on an aggregation of the first patch and of a second patch generated by a second computing node.

The specification of the present disclosure relates to a device, method, and computer program for searching for genes in a synthetic lethality relationship by using a Gaussian restricted Boltzmann machine. According to any one of the above-described means for solving the problem, a gene in a synthetic lethality relationship with a target gene may be output through an artificial intelligence model trained by receiving a training data set including an mRNA expression value in RNA Seq data, the presence or absence of a mutation in WES data, and a dependency score in CRISPR KO data. In addition, it is possible to increase the efficiency of anticancer treatment by using the genes in a synthetic lethality relationship calculated by using the trained artificial intelligence model. In addition, it is possible to present a treatment route with low drug resistance by using the genes in a synthetic lethality relationship calculated by using the trained artificial intelligence model.

The invention relates to the technical field of computer data processing and artificial intelligence, and discloses a multi-type variable adaptive CRBM digital twinning modeling method, device and medium, the method comprises the following steps: obtaining modeling data and defining the modeling data as visible, conditional and hidden variable sets, the visible variables comprising non-standard distribution types; constructing a condition-restricted Boltzmann machine model, and directly constructing corresponding conditional probability distribution and interaction energy items according to original probability distribution characteristics for visible variables of non-standard distribution types; performing parameter updating on the model by calculating a weighted combination of a likelihood gradient and an adversarial gradient by adopting an adversarial training mechanism in which adversarial items are introduced; and using the trained model to generate digital twin data through Gibbs sampling based on a given condition input variable. According to the method, heterogeneous data can be directly processed, the original statistical characteristics of the data are reserved, and the precision of model parameter estimation and the fidelity of generated data are improved.

The invention discloses a partial discharge fault diagnosis method and system based on deep confidence, and aims to improve the precision of partial discharge fault diagnosis of power equipment. According to the method, an original binary partial discharge time sequence PRPS signal is read, an effective section is intercepted, amplitude transformation is carried out, and a de-duplicated and normalized fault diagnosis data set is constructed; a multi-layer convolution restricted Boltzmann machine structure is adopted, and an unsupervised contrast divergence algorithm is combined for layer-by-layer pre-training, so that automatic extraction of deep time sequence features of the partial discharge signals is realized; further stacking to form a convolutional deep belief network, connecting a classifier, and performing supervised fine tuning training on the network by using a cross entropy loss function with category weight; in the reasoning stage, normalization processing is carried out on a signal to be diagnosed, features are extracted through the pre-training network, and finally a predicted fault category is output according to the classifier.

The application discloses a multi-modal critical edge biomarker identification method, takes an individual cancer patient as a dynamic network system, combines a dynamic network theory biomarker theory and a multi-modal evolutionary algorithm, performs hidden space search by using a restricted Boltzmann machine on the basis of an MMPDNB model, and is a new multi-modal PDENB identification model. Firstly, a PEN of the cancer individual patient is constructed. Then, an optimization objective function is designed. Finally, a multi-modal optimization algorithm is used to search for a PDENB set. The application can not only promote the researches on a mathematical model and an algorithm design of the PDENB identification problem, but also help to understand the individual heterogeneity of the cancer, and realize the early diagnosis and treatment of the cancer individual patient.

The invention provides a transformer capacitance compensation through-current method and system based on deep learning, and relates to the technical field of reactive power compensation of a power system. The method comprises the following steps: acquiring a transformer signal to obtain standardized input data; modeling of the double-layer restricted Boltzmann machine is completed; generating a deep hidden feature vector; generating a capacitor switching parameter; the control hardware module receives a control instruction and triggers a relay to act; and comparing the reconstruction error with a preset threshold value, maintaining the current switching state when the reconstruction error does not exceed the threshold value, and regenerating and adjusting the capacitor switching parameter when the reconstruction error exceeds the threshold value. According to the method, a complete process from standardized input, feature modeling and capacitance parameter mapping to control execution and error closed-loop updating is constructed, adaptive adjustment of capacitance switching action and whole-process data retention are supported, and decision consistency and execution stability under complex working conditions are improved.

The invention provides a twin self-calibration photoelectric probability bit circuit unit and a preparation and use method thereof, the circuit unit comprises a self-calibration photoelectric differential module, a voltage comparator and a reference voltage source, the self-calibration photoelectric differential module is a pair of photoelectric indium gallium zinc oxide (IGZO) thin film transistors which are tightly coupled in space; the device is patterned through an ultraviolet lithography process and a radio frequency magnetron sputtering process at the same time, atomic-scale matching of a reference tube and a photosensitive tube in geometric dimension, film thickness and interface state density is ensured, differences only exist in illumination conditions, common-mode interferences such as aging caused by temperature drift and bias stress are offset by effectively utilizing a difference principle, and the performance of the device is improved. The physical characteristic of highly consistent aging trend is utilized to construct a synchronous drifting series voltage division network, and high-precision and high-stability in-situ photoelectric probability calculation is realized; probability bits are generated through dual modulation of grid voltage and light intensity, and the method is suitable for Bayesian reasoning, restricted Boltzmann machines and other probabilistic neural network hardware.

Reinforcement learning with space state pruning is disclosed. States of an environment used in training a reinforcement learning model are pruned using a restricted Boltzmann Machine. Reducing the number of states, by pruning, reduces time to convergence.